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To bridge the knowledge gap between (a) our (instantaneous‐to‐seasonal‐scale) process understanding of plants and water and (b) our projections of long‐term coupled feedbacks between the terrestrial water and carbon cycles, we must uncover what the dominant dynamics are linking fluxes of water and carbon. This study uses the simplest empirical dynamical systems models—two‐dimensional linear models—and observation‐based data from satellites, eddy covariance towers, weather stations, and machine‐learning‐derived products to determine the dominant sub‐annual timescales coupling carbon uptake and (normalized) evaporation fluxes. We find two dominant modes across the Contiguous United States: (1) a negative correlation timescale on the order of a few days during which landscapes dry after precipitation and plants increase their carbon uptake through photosynthetic upregulation. (2) A slow, seasonal‐scale positive covariation through which landscape drying leads to decreased growth and carbon uptake. The slow (positively correlated) process dominates the joint distribution of local water and carbon variables, leading to similar behaviors across space, biomes, and climate regions. We propose that vegetation cover/leaf area variables link this behavior across space, leading to strong emergent spatial patterns of water/carbon coupling in the mean. The spatial pattern of local temporal dynamics—positively sloped tangent lines to a convex long‐term mean‐state curve—is surprisingly strong, and can serve as a benchmark for coupled Earth System Models. We show that many such models do not represent this emergent mean‐state pattern, and hypothesize that this may be due to lack of water‐carbon feedbacks at daily scales. [ABSTRACT FROM AUTHOR]

Terrestrial gross primary productivity (GPP) is the largest carbon flux in the global carbon cycle and plays a crucial role in terrestrial carbon sequestration. However, historical and future global GPP estimates still vary markedly. In this study, we reduced uncertainties in global GPP estimates by employing an innovative emergent constraint method on remote sensing‐based GPP datasets (RS‐GPP), using ground‐based estimates of GPP from flux towers as the observational constraint. Using this approach, the global GPP in 2001–2014 was estimated to be 126.8 ± 6.4 PgC year−1, compared to the original RS‐GPP ensemble mean of 120.9 ± 10.6 PgC year−1, which reduced the uncertainty range by 39.6%. Independent space‐ and time‐based (different latitudinal zones, different vegetation types, and individual year) constraints further confirmed the robustness of the global GPP estimate. Building on these insights, we extended our constraints to project global GPP estimates in 2081–2100 under various Shared Socioeconomic Pathway (SSP) scenarios: SSP126 (140.6 ± 9.3 PgC year−1), SSP245 (153.5 ± 13.4 PgC year−1), SSP370 (170.7 ± 16.9 PgC year−1), and SSP585 (194.1 ± 23.2 PgC year−1). These findings have important implications for understanding and projecting climate change, helping to develop more effective climate policies and carbon reduction strategies. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is a crucial link in water circulation. It plays an important role in the global water cycle and surface energy balance, and has significant impacts on climate, ecosystems, and water resources management. Therefore, the quality of evapotranspiration data is crucial for the precise management of global water resources. This study conducted accuracy validation and spatiotemporal comparison of three ET products in the Northern Hemisphere, selecting the ET products that is more suitable for the Northern Hemisphere, providing suggestions for strengthening the combination of remote sensing and ground observation research. Using the monthly average measured data from FluxNet2015 flux sites to verify three ET products, it was found that PML_ V2 product has the highest accuracy in the Northern Hemisphere, followed by GLDAS, and finally MOD16A2, with correlation coefficients R of 0.66, 0.57, and 0.56, respectively; The root mean square error (RMSE) is 2.46, 5.68, and 12.42 mm/month, respectively; The average biases are 14.36%, 16.86%, and 35.02%, respectively. The GLDAS ET product has the ability to monitor daily scale ET, and the consistency between the daily average estimated value and the measured value at the flux tower site is high. The correlation coefficient R is 0.74, and the RMSE and Bias are 1.62mm/day and 27.90%, respectively. Overall, on the time scale, all three ET products can simulate the seasonal changes in the Northern Hemisphere, with higher summer evapotranspiration and lower winter evapotranspiration. The three ET products in summer all have overestimation phenomena on different land cover types, and the simulation results in other seasons are better than the ground observation values. Among them, MOD16A2 performs the worst, and the overestimation phenomenon is the most obvious. In addition, during the period from 2001 to 2020, except for arid areas, the spatial distribution of the three ET products was relatively consistent in most regions, with a correlation coefficient R greater than 0.6. This study provides scientific recommendations for selecting suitable ET data sources for conducting evapotranspiration studies in the Northern Hemisphere by evaluating the uncertainty and product quality of different ET products. [ABSTRACT FROM AUTHOR]

Analysis of measured evapotranspiration shows that subsurface plant-accessible water storage (PAWS) can sustain evapotranspiration through multiyear dry periods. Measurements at 25 flux tower sites in the semiarid western United States, distributed across five land cover types, show both resistance and vulnerability to multiyear dry periods. Average (±standard deviation) evapotranspiration ranged from 660 ± 230 mm yr −1 (October–September) in evergreen needleleaf forests to 310 ± 200 mm yr −1 in grasslands and shrublands. More than 52% of the annual evapotranspiration in Mediterranean climates is supported on average by seasonal drawdown of subsurface PAWS, versus 29% in monsoon-influenced climates. Snowmelt replenishes dry-season PAWS by as much as 20% at sites with significant seasonal snow accumulation but was insignificant at most sites. Evapotranspiration exceeded precipitation in more than half of the observation years at sites below 35°N. Annual evapotranspiration at non-energy-limited sites increased with precipitation, reaching a mean wet-year evapotranspiration of 833 mm for evergreen needleleaf forests, 861 mm for mixed forests, 558 mm for woody savannas, 367 mm for grasslands, and 254 mm for shrublands. Thirteen sites experienced at least one multiyear dry period, when mean precipitation was more than one standard deviation below the historical mean. All vegetation types except evergreen needleleaf forests responded to multiyear dry periods by lowering evapotranspiration and/or significant year-over-year depletion of subsurface PAWS. Sites maintained wet-year evapotranspiration rates for 8–33 months before attenuation, with a corresponding net PAWS drawdown of as much as 334 mm. Net drawdown at many sites continued until the dry period ended, resulting in an overall cumulative withdrawal of as much as 558 mm. Evergreen needleleaf forests maintained high evapotranspiration during multiyear dry periods with no apparent PAWS drawdown; these forests currently avoid drought but may prove vulnerable to longer and warmer dry periods that reduce snowpack storage and accelerate evapotranspiration.

Ecosystems are responsible for strong feedback processes that affect climate. The mechanisms and consequences of this feedback are uncertain and must be studied to evaluate their influence on global climate change. The main objective of this study is to assess the gross primary production (GPP) dynamics and the energy partitioning patterns in three different European forest ecosystems through time series analysis. The forest types are an Evergreen Needleleaf Forest in Finland (ENF_FI), a Deciduous Broadleaf Forest in Denmark (DBF_DK), and a Mediterranean Savanna Forest in Spain (SAV_SP). Buys-Ballot tables were used to study the intra-annual variability of meteorological data, energy fluxes, and GPP, whereas the autocorrelation function was used to assess the inter-annual dynamics. Finally, the causality of GPP and energy fluxes was studied with Granger causality tests. The autocorrelation function of the GPP, meteorological variables, and energy fluxes revealed that the Mediterranean ecosystem is more irregular and shows lower memory in the long term than in the short term. On the other hand, the Granger causality tests showed that the vegetation feedback to the atmosphere was more noticeable in the ENF_FI and the DBF_DK in the short term, influencing latent and sensible heat fluxes. In conclusion, the impact of the vegetation on the atmosphere influences the energy partitioning in a different way depending on the vegetation type, which makes the study of the vegetation dynamics essential at the local scale to parameterize these processes with more detail and build improved global models.

The relationship between food production and the preservation of the environment has become a topic of international debate, mainly related to greenhouse gas emissions. In this way, it became increasingly important to know potential sources and sinks of these gases, mainly evaluating the carbon balance in the area of agriculture and livestock. In Rio Grande do Sul, agricultural activity is extremely important culturally and economically, occupying a large part of the state, mainly within the Pampa biome, which has large areas of natural pastures capable of serving as fodder for animals. In this work, we quantified the exchange of CO2 through the Eddy Covariance methodology in two experimental sites of native pasture of the Pampa biome used for cattle raising, in the rotating system, which aims to maintain the diversity of the native pasture. With one-year data we showed that both sites were CO2 sinks, ranging from -82,4 g C m-2 a-1 to -282,3 g C m-2 a-1, due to differences in environmental conditions and vegetation composition. Thus, raising cattle on the natural pasture of the pampa biome can contribute to offsetting greenhouse gas emissions in other agricultural sectors. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications. [ABSTRACT FROM AUTHOR]

The Congo basin is home to the second-largest tropical forest in the world. Therefore, it plays a crucial role in the regional water cycle, the global carbon cycle and the continental greenhouse gas balance. Yet very few field-based data on related processes exist. In the wake of global change, there is a need for a better understanding of the current and future response of the forest biome in this region. A new long-term effort has been set up to measure the exchange of greenhouse gasses between a humid lowland tropical forest in the Congo basin and the atmosphere via an eddy-covariance (EC) tower. Eddy-covariance research stations have been used for decades already in natural and man-made ecosystems around the globe, but the natural ecosystems of Central Africa remained a blind spot. The so-called "CongoFlux" research site has been installed right in the heart of the Congo Basin, at the Yangambi research center in DR Congo. This introductory paper presents an elaborated description of this new greenhouse gas research infrastructure; the first of its kind in the second-largest tropical forest on Earth. [ABSTRACT FROM AUTHOR]

The Congo basin is home to the second-largest tropical forest in the world. Therefore, it plays a crucial role in the regional water cycle, the global carbon cycle and the continental greenhouse gas balance. Yet very few field-based data on related processes exist. In the wake of global change, there is a need for a better understanding of the current and future response of the forest biome in this region. A new long-term effort has been set up to measure the exchange of greenhouse gasses between a humid lowland tropical forest in the Congo basin and the atmosphere via an eddy-covariance (EC) tower. Eddy-covariance research stations have been used for decades already in natural and man-made ecosystems around the globe, but the natural ecosystems of Central Africa remained a blind spot. The so-called “CongoFlux” research site has been installed right in the heart of the Congo Basin, at the Yangambi research center in DR Congo. This introductory paper presents an elaborated description of this new greenhouse gas research infrastructure; the first of its kind in the second-largest tropical forest on Earth.

Re-suspended road dust is one of the main urban atmospheric pollutants. Thus, systematic management of polluted roads is essential, which requires precise measurement of re-suspended road dust emission factors. A mobile laboratory (ML) and a flux tower (FT) were used to measure re-suspended road dust. Re-suspended road dust concentration was measured while driving the ML at speeds of 40, 50, and 60 km/h along straight sections of five different roads in Incheon City, Korea. The correlation between measurements using the ML and the FT was anlyzed according to the ML speed. The results revealed a linear relationship between the mass concentration measured by the ML and the emission factor obtained from the FT. At faster ML speeds, the same emission factor was associated with higher concentrations measured by the ML. Thus, if the ML measurements are adjusted for the ML speed by employing the methods of this study, re-suspended road dust emission factors can be measured more accurately. Based on these findings, it is expected that the use of the MLs will contribute to reducing re-suspended road dust by enabling more effective management of road pollution levels. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration (ETa) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration (ETr) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

• LiDAR-based roughness parameterization is introduced in Evapotranspiration (ET) model. • Original SEBS overestimate LE and underestimate H compared to in-situ EC observations. • SEBS LiDAR perform better than SEBS in estimating LE and H compared to EC observations. • Canopy height controls the output of SEBS ET model. • LiDAR can significantly improve the accuracy of roughness lengths in ET model. Spatially distributed information of actual Evapotranspiration (ET) is essentially required for various applications in agriculture, as well as studies related to water balance. An Energy Balance model that uses optical and thermal remote sensing datasets is appropriate for the spatially distributed estimation of ET, which is primarily controlled by local environmental conditions. In this study, we use the Surface Energy Balance System (SEBS), which is an Energy Balance model, to estimate an improved actual ET, by integrating canopy height information obtained from ICESat/GLAS, the first space-borne LiDAR satellite. Our model estimates latent heat flux (LE) as a residual of energy balance, while sensible heat flux (H) is estimated based on Monin–Obukhov similarity theory. The spatial variability of H and LE estimated from Landsat TM/ETM+ images were compared with eddy covariance (EC) based in situ flux tower observations, in order to document the SEBS model uncertainties in three forest sites in the East and Southeast Asian ecosystem. We identified that canopy height (h c) is one of the critical parameters that can induce uncertainties in SEBS model estimations. Therefore, h c obtained from LiDAR was introduced in parameterization of the roughness length for momentum transfer (z 0m). In situ based h c obtained from flux tower stations, and the normalized difference vegetation index based formulation of h c proposed by Su (2001), were also introduced in the SEBS z 0m parameterization. Root mean square error of LiDAR integrated SEBS (SEBS LiDar) for estimated H values reduced by (20, 45, and 23)% compared to Landsat integrated SEBS (SEBS Landsat), and (18, 7, and 9)% compared to In situ integrated SEBS (SEBS Insitu) at the three selected forest sites (SMC, QYZ, and SMF), respectively, while the mean coefficient of determination values for the estimated LE improved by (9.09 and 5.88)% compared to SEBS Landsat and SEBS Insitu , respectively, with reference to ground-based flux tower observations. Our analysis reveals that LiDAR-based parameterization of z 0m can significantly increase the accuracy of estimated roughness lengths within an ET model, which can therefore improve the accuracy of the estimated turbulent heat fluxes. SEBS LiDAR was further evaluated for the spatially distributed mapping of actual ET. We have observed that ET estimated by SEBS Landsat overestimated in forest sites compared to EC observations; however, LiDAR-integrated SEBS marginally improved the accuracy of ET, thereby leading to an improvement in the mean correlation value of 0.87 and mean normalized standard deviation value of 0.93 approaching unity based on Taylor diagram, which more closely approximates the actual ground-based measurements. The proposed methodology overcomes the current limitation in the energy balance models in estimating roughness lengths, by providing accurate formulation of h c by specifically incorporating LiDAR measurements in areas of tall vegetation canopies. The operational improvement in roughness lengths by next-generation SEBS LiDAR model provides an opportunity to improve the estimation accuracy of water and energy fluxes, specifically ET. Overall, our proposed SEBS LiDAR model provides a benchmark that is useful to policy makers and water resource managers in devising a plan for sustainable use of water resources in the East Asian region, as well as other similar ecosystems. [ABSTRACT FROM AUTHOR]

• The study offers insights into atmospheric turbulence during intense grass fires. • These findings are significant as they provide the first-ever in-situ turbulence data collected during Red Flag Warnings. • The data establishes an upper limit for turbulence in prescribed grass fires, offering valuable reference points. • These results have the potential to enhance turbulence schemes in fire behavior models, contributing to better fire predictions. • This study underscores the complexities of studying fire type effects in a real-world context. A wildland fire experiment was conducted on a 40-ha tall grass prairie in Texas to study fire-atmosphere interactions. The experiment coincided with a Red Flag Warning, signifying specific weather conditions associated with an elevated risk of wildfire ignition and rapid spread. This provided a rare opportunity to observe wildland fire behavior and its impact on atmospheric flows under conditions deliberately avoided in similar experiments. This study analyzed the high frequency meteorological data collected during the experiment using three-dimensional sonic anemometers mounted on a 43-m tower (at 5.8, 10 and 20 m) and on three 10-m towers (at 5.3 m) within the burn block. The tower placements were carefully chosen to capture the impact of both head and flank fires on the mean and turbulent flows. The hypothesis is that fire-induced perturbations in the lower atmosphere, quantified through parameters like turbulent kinetic energy (TKE) and vertical fluxes, would be substantially stronger than those observed in similar wildland fires under less fire-prone atmospheric conditions. While this hypothesis was validated for heat flux and TKE, it did not hold for momentum fluxes or friction velocity. Additionally, the observations failed to confirm the hypothesis that head fires would produce more intense perturbations than flank fires. Given the homogeneity of fuel, terrain and meteorological elements across the burn unit, this underscores the challenge of disentangling the effects of fire type from other natural factors in a real-world setting. Nevertheless, the finding holds promise for improving turbulence parameterizations in fire-behavior and smoke dispersion models. [ABSTRACT FROM AUTHOR]

Concentration of road dust re-suspended by passing vehicles can be measured using fine-dust measuring devices installed at various heights of a flux tower. In this study, simulations were performed to analyze the distribution of dust plumes along the height of a flux tower according to its distance from the center of the road, the vehicle speed, wind direction, and wind speed. Simulation results were compared with field measurements of re-suspended road dust collected by a flux tower during the passage of a van-type mobile laboratory vehicle to confirm the accuracy of the simulation. A parametric study was then conducted using the simulation of the mobile lab vehicle to evaluate the effects of vehicle speed, wind direction, and wind speed. To accurately capture re-suspended road dust concentrations, the flux tower must be at least 4.5, 5.1, 5.8, and 6.2 m high when located 2, 4, 6, and 8 m, respectively, from the road center. The results of this study are expected to serve as guidelines in determining the location and height of flux towers when measuring re-suspended road dust. [ABSTRACT FROM AUTHOR]

Surface energy balance (SEB) models based on thermal remote sensing data are widely used in research applications to map evapotranspiration (ET) across various landscapes. However, their ability to capture ET from winter wheat remains underexplored, especially in practical applications such as integrated resource management and drought preparedness. Investigating winter wheat ET dynamics is important in agricultural regions such as the Southern Great Plains of the U.S., where winter wheat is extensively cultivated. The goal of this study was to evaluate the performance of five fully automated SEB models, three contextual-based (CB) and two pixel-based (PB), in estimating instantaneous and daily ET of winter wheat by comparing the model results with flux tower observations. The CB models included Surface Energy Balance Algorithm for Land (SEBAL), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC), and Triangular Vegetation Temperature (TVT). The PB models included Surface Energy Balance System (SEBS) and Two-Source Energy Balance (TSEB). Model evaluation during two winter wheat growing seasons (2016-2018) using 28 Landsat images showed that the instantaneous ET estimates from METRIC and TSEB had the smallest (RMSE = 0.14 mm h-1) and largest (RMSE = 0.27 mm h-1) errors, respectively. At the daily scale, SEBAL was the best performing model (RMSE = 1.0 mm d-1), followed by TVT (RMSE = 1.1 mm d-1), METRIC (RMSE = 1.2 mm d-1), SEBS (RMSE = 1.3 mm d-1), and TSEB (RMSE = 1.5 mm d-1). Overall, the CB models provided smaller errors than the PB models. Larger errors in daily ET estimation were observed during low vegetation and drier conditions, especially for the PB models. [ABSTRACT FROM AUTHOR]

There have been vociferous calls for 'tree-centered' vegetation models to refine predictions of forest carbon (C) cycling. Unfortunately, our global survey at flux-tower sites indicates insufficient empirical data support for this much-needed model development. We urge for a new generation of studies across large environmental gradients that strategically pair long-term ecosystem monitoring with manipulative experiments on mature trees. For this, we outline a versatile experimental framework to build cross-scale data archives of C uptake and allocation to structural, non-structural, and respiratory sinks. Community-wide efforts and discussions are needed to implement this framework, especially in hitherto underrepresented tropical forests. Global coordination and realistic priorities for data collection will thereby be key to achieve and maintain adequate empirical support for tree-centered vegetation modeling. Vegetation models are converging on an intermediate complexity with the tree level as the target for simulation. Field and laboratory observations of forest ecosystem processes are indispensable to parameterize, evaluate, or be assimilated into tree-centered vegetation models. Observations of C allocation in trees are not being developed at the same pace as tree-centered vegetation models, lack scalability, and are scarce in tropical and boreal forests. Flux towers measure the net C gain or loss of ecosystems at very high temporal resolution and provide an advanced setup for monitoring meteorological, biogeochemical, and ecological variables. Experimental manipulations of mature trees are methodologically challenging, but recent advances have provided unprecedented insight in C uptake, respiration, and allocation to structural and non-structural sinks in trees under climate change. [ABSTRACT FROM AUTHOR]

Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.

Background: Earth Observation 'EO' remote sensing technology development enables original insights into vegetation function and health at ever finer temporal, spectral and spatial resolution. Research sites equipped with monitoring infrastructure such as flux towers operate at a key bridging scale between satellite platform measurements and on-the-ground leaf-level processes. Results: This paper presents the technical details of the design and operation of a proximal observation system 'THEMS' that generates unattended long-term high quality thermal and hyperspectral images of a forest canopy on a short (sub-daily) timescale. The primary purpose of the system is to measure canopy temperature, spectral reflectance and radiance coincident with a highly instrumented flux tower site for benchmarking purposes. Basic system capability is demonstrated through low level data product descriptions of the high-resolution multi-angular imagery and ancillary data streams. The system has been successfully operational for more than 2 years with little to no intervention. Conclusions: These data can then be used to derive remotely sensed proxies of canopy and ecosystem function to study temporal forest dynamics over a wide range of wavelengths, spatial scales (individual trees to canopy), and temporal scales (minutes to multiple years). The multi-purpose system is intended to provide unprecedented spatio-temporal ecophysiological insight and to underpin upscaling of remotely sensed dynamic ecosystem water, CO2, and energy exchange processes. [ABSTRACT FROM AUTHOR]

The eddy covariance technique, commonly applied using flux towers, enables the investigation of greenhouse gas (e.g., carbon dioxide, methane, nitrous oxide) and energy (latent and sensible heat) fluxes between the biosphere and the atmosphere. Through measuring carbon fluxes in particular, eddy covariance flux towers can give insight into how ecosystem scale photosynthesis (i.e., gross primary productivity) changes over time in response to climate and management. This chapter is designed to be a beginner's guide to understanding the eddy covariance method and how it can be applied in photosynthesis research. It introduces key concepts and assumptions that apply to the method, what materials are required to set up a flux tower, as well as practical advice for site installation, maintenance, data management, and postprocessing considerations. This chapter also includes examples of what can go wrong, with advice on how to correct these errors if they arise. This chapter has been crafted to help new users design, install, and manage the best towers to suit their research needs and includes additional resources throughout to further guide successful eddy covariance research activities., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Highlights • Ten machine learning methods were tested for retrieval of evapotranspiration from remote sensing. • The best results were obtained with bootstrap aggregation tree, multi-layer neural network, and random kernel methods. • Training with same surface type data did not improve algorithm performance. Abstract Remote sensing retrieval of evapotranspiration (ET), or surface latent heat exchange (LE), is of great utility for many applications. Machine learning (ML) methods have been extensively used in many disciplines, but so far little work has been performed systematically comparing ML methods for ET retrieval. This paper provides an evaluation of ten ML methods for estimating daily ET based on daily Global LAnd Surface Satellite (GLASS) radiation data and high-level Moderate-Resolution Imaging Spectroradiometer (MODIS) data products and ground measured ET data from 184 flux tower sites. Measurements of accuracy (RMSE, R2, and bias) and run time were made for each of ten ML methods with a smaller training data set (n = 7910 data points) and a larger training data set (n= 69,752 data points). Inclusion of more input variables improved algorithm performance but had little effect on run time. The best results were obtained with the larger training data set using the bootstrap aggregation (bagging) regression tree (validation RMSE = 19.91 W/m2) and three hidden layer neural network (validation RMSE = 20.94 W/m2), although the less computationally demanding random kernel (RKS) algorithm also produced good results (validation RMSE = 22.22 W/m2). Comparison of results from sites with different ecosystem types showed the best results for evergreen, shrub, and grassland sites, and the weakest results for wetland sites. Generally, performance was not improved by training with data from only the same ecosystem type. [ABSTRACT FROM AUTHOR]

In this paper, we address the retrieval of spatially distributed latent heat flux ( λ E) over a tropical dry forest using multi-spectral and thermal unmanned aerial vehicle (UAV) imagery. The study was carried out in the Santa Rosa National Park Environmental Monitoring Super-Site, Costa Rica, in June 2016. The triangle method was used to derive λ E from the UAV imagery and the results were compared to λ E measurements of an eddy covariance system within the coincident eddy flux tower footprint. The tower footprint was derived using a two-dimensional parameterization model for flux footprint prediction. The comparisons with the flux tower measurements showed a mean relative difference of 10.98% with a slight overestimation of the UAV-based flux retrievals by nearly 7.7 Wm − 2 . The results are in good agreement with satellite-based retrievals, as provided by the literature, for which the triangle method was initially developed and mostly used so far. This study proved to be a promising approach for transferring the triangle method to UAV imagery in ecosystems such as tropical dry forests. With the presented approach, new details in spatially distributed latent heat flux estimates at ultra-high resolution are now possible, thereby potentially closing the gap in spatial resolution between satellites and flux towers. Even more, it allows tracing the latent heat flux from single trees at leaf level. Besides, this approach also opens new perspectives for the monitoring of latent heat fluxes in tropical dry forests.

Recent advances in remote sensing technology and methods have resulted in the development of an evapotranspiration (ET) product from the Moderate Resolution Imaging Spectrometer (MOD16). The accuracy of this product however has not been tested for coastal wetland ecosystems. The objective of this study therefore is to validate the MOD16 ET product using data from one eddy covariance flux tower situated in the Panjin coastal wetland ecosystem within the Liaohe River Delta, Northeast China. Cumulative ET data over an eight-day period in 2005 from the flux tower was calculated to coincide with the MOD16 products across the same period. Results showed that data from the flux tower were inconsistent with that gained form the MOD16 ET. In general, results from Panjin showed that there was an underestimation of MOD16 ET in the spring and fall, with Biases of-2.27 and-3.53 mm/8d, respectively (-40.58% and-49.13% of the observed mean). Results for Bias during the summer had a range of 1.77 mm/8d (7.82% of the observed mean), indicating an overestimation of MOD16 ET. According to the RMSE, summer (6.14 mm/8d) achieved the lowest value, indicating low accuracy of the MOD16 ET product. However, RMSE (2.09 mm/8d) in spring was the same as that in the fall. Relationship between ET and its relevant meteorological parameters were analyzed. Results indicated a very good relationship between surface air temperature and ET. Meanwhile a significant relationship between wind speed and ET also existed. The inconsistent comparison of MOD16 and flux tower-based ET are mainly attributed to the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. MODIS pixels. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration is assessed via surface energy balance at an hourly rate. However, a robust estimation of daily evapotranspiration from hourly values is required. Outcomes of surface energy balance are frequently determined via measures of eddy covariance latent heat flux. Surface energy balance can be applied on images acquired at different times and spatial resolutions. In addition, hourly actual evapotranspiration needs to be integrated at a daily rate for operational uses. Questions arise whether the validation of surface energy balance models can benefit from complementary in situ measures of latent heat flux and sap flow transpiration. Here, validation was driven by image acquisition time, spatial resolution, and temporal integration. Thermal and optical images were collected with a proximity-sensing platform on an olive orchard at different acquisition times. Actual latent heat fluxes from canopy and sap flux at tree trunks were measured with a flux tower and heat dissipation probes. The latent heat fluxes were then further analyzed. A surface energy balance was applied over proximity sensing images re-sampled at different spatial resolutions with resulting latent heat fluxes compared to in situ ones. A time lag was observed and quantified between actual latent heat fluxes from canopy and sap flux at the tree trunk. Results also indicate that a pixel resolution comparable to the average canopy size was suitable for estimating the actual evapotranspiration via a single source surface energy balance model. Images should not be acquired at the beginning or the end of the diurnal period. Findings imply that sap flow transpiration can be used to measure surface energy balance at a daily rate or when images are found at an hourly rate near noon, and the existing time lag between the latent heat flux at the canopy and the sap flow at the trunk does not need to be taken into account. [ABSTRACT FROM AUTHOR]

Globally, water is an important resource required for the survival of human beings. Water is a scarce resource in the semi-arid environments, including South Africa. In South Africa, several studies have quantified evapotranspiration (ET) in different ecosystems at a local scale. Accurate spatially explicit information on ET is rare in the country mainly due to lack of appropriate tools. In recent years, a remote sensing ET product from the MODerate Resolution Imaging Spectrometer (MOD16) has been developed. However, its accuracy is not known in South African ecosystems. The objective of this study was to validate the MOD16 ET product using data from two eddy covariance flux towers, namely; Skukuza and Malopeni installed in a savanna and woodland ecosystem within the Kruger National Park, South Africa. Eight day cumulative ET data from the flux towers was calculated to coincide with the eight day MOD16 products over a period of 10 years from 2000 to 2010. The Skukuza flux tower results showed inconsistent comparisons with MOD16 ET. The Malopeni site achieved a poorer comparison with MOD16 ET compared to the Skukuza, and due to a shorter measurement period, data validation was performed for 2009 only. The inconsistent comparison of MOD16 and flux tower-based ET can be attributed to, among other things, the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. MODIS pixel. MOD16 is important for global inference of ET, but for use in South Africa's integrated water management, a locally parameterized and improved product should be developed.

Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (Pinus sylvestris) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O2 and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA 2 = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (ΔF/Fm′), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.

Reference evapotranspiration (ET0) is an agrometeorological variable widely used in hydrology and agriculture. The FAO-56 Penman-Monteith combination method (PM method) is a standard for computing ET0 for water management. However, this scheme is limited to areas where climatic data with good quality are available. Maps of 10-day averaged ET0 at 5 km × 5 km grid spacing for the Taiwan region were produced by multiplying pan evaporation (Epan), derived from ground solar radiation (GSR) retrieved from satellite images using the Heliosat-3 method, by a fixed pan coefficient (Kp). Validation results indicated that the overall mean absolute percentage error (MAPE) and normalized root-mean-square deviation (NRMSD) were 6.2 and 7.7%, respectively, when compared with ET0 computed by the PM method using spatially interpolated 10-day averaged daily maximum and minimum temperature datasets and GSR derived from satellite inputs. Land coefficient (KL) values based on the derived ET0 estimates and long term latent heat flux measurements, were determined for the following landscapes: Paddy rice (Oryza sativa), subtropical cypress forest (Chamaecyparis obtusa var. for mo Sana and Chamaecyparis formosensis), warm-to-temperate mixed rainforest (Cryptocarya chinensis, Engelhardtia roxburghiana, Tutcheria shinkoensis, and Helicia formosana), and grass marsh (Brachiaria mutica and Phragmites australis). The determined land coefficients are indispensable to scale ET0 in estimating regional evapotranspiration. [ABSTRACT FROM AUTHOR]

Old-growth (>100 years old) boreal forests are recognized as having accumulated large inventories of terrestrial carbon, but the magnitude of annual net exchange is not well established. Eddy covariance measurements of carbon dioxide exchange were made at a flux tower at the Northern Old Black Spruce forest in Manitoba, Canada, from 1994 to 2008. We evaluated the uncertainty in estimates of annual net ecosystem production by comparing four methods of filling missing measurement periods. Three of the methods were previously developed by groups at Harvard University, the Fluxnet Canada Research Network, and the Max Planck Institute of Biogeochemistry; and the fourth method used the mean of measurements from the dataset selected for the same time and day. Two methods showed carbon losses near the beginning of the period but all four methods estimated carbon gains in the later years. Individual years and methods ranged ±90 g C m −2 y −1 , demonstrating a wide range of estimates if data had been available from only a single year and a single method. When averaged over multiple years, the mean and standard error among methods gave a net forest carbon sink of 29 ± 10 g C m −2 y −1 , ranging from 4 to 48 g C m −2 y −1 . Only the upper (Mean Data and Max Planck Institute of Biogeochemistry methods) and lower (Harvard method) estimates were significantly different, with the Fluxnet Canada Research Network method being the same as all other methods. There was no difference among methods for the January to August period, or for daytime periods. All methods were statistically different from a carbon-neutral forest suggesting that the Northern Old Black Spruce forest was likely a small carbon sink. Methods that estimated high ecosystem respiration also estimated high gross ecosystem production, and the small net differences could not be easily attributed to differences in flux partitioning. For forests that are close to carbon neutral, the use of several gap-filling methods: increases confidence in conclusions regarding the ecosystem carbon balance. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is expected to increase by a considerable amount because of the impact of future temperature increase. Nowadays, the daily to seasonal ET maps can be used to provide information for a sustainable and adaptive watershed eco-environment. This study attempts to estimate the spatial ET of South Korea (99,900 km2), located within the latitudes of 33°06'N to 43°01'N and the longitudes of 124°04'E to 131°05'E, on a daily basis. The satellite-based image-processing model Surface Energy Balance Algorithms for Land (SEBAL) was adopted and modified to generate the spatial ET data. The SEBAL was calibrated using two years (2012-2013) of measured ETs by an eddy covariance (EC) flux tower at three locations (two in a mixed forest area and one in a rice paddy area). The primary inputs for the model were land surface temperature/emissivity (LST/E), the Normalized Distribution Vegetation Index (NDVI), albedo (Ab) from a Terra Moderate-resolution Imaging Spectroradiometer (MODIS) satellite, a digital elevation model, and wind speed and solar radiation (Rs) from 76 ground-based weather stations. When LST data were unavailable because of clouds and/or snow, the bias-corrected ground temperature measured at the weather stations was used. The NDVI and Ab were used as the monthly average value to maintain relatively stable values rather than using the original time interval data. The determination coefficient (R2) between SEBAL and the flux tower ET was 0.45-0.54 for the two mixed forest towers and 0.79 for the rice paddy tower reflecting the known characteristics of closed and open space ET estimation. The spatial distribution of SEBAL showed that the spatial ET reflected the geographical characteristics, revealing that the ET of lowland areas was higher than that of highland areas. [ABSTRACT FROM AUTHOR]

Net ecosystem exchange (NEE) of carbon dioxide (CO2) was measured in a cool temperate peatland in northwestern Turkey on a continuous basis using eddy covariance (EC) sensors and multiple (non-)linear regression-M(N)LR-models. Our results showed that hourly NEE varied between −1.26 and 1.06 mg CO2 m−2 s−1, with a mean value of 0.11 mg CO2 m−2 s−1. Nighttime ecosystem respiration (RE) was on average measured as 0.23 ± 0.09 mg CO2 m−2 s−1. Two best-fit M(N)LR models estimated daytime RE as 0.64 ± 0.31 and 0.24 ± 0.05 mg CO2 m−2 s−1. Total RE as the sum of nighttime and daytime RE ranged from 0.47 to 0.87 mg CO2 m−2 s−1, thus yielding estimates of gross primary productivity (GPP) at −0.35 ± 0.18 and −0.74 ± 0.43 mg CO2 m−2 s−1. Use of EC sensors and M(N)LR models is one of the most direct ways to quantify turbulent CO2 exchanges among the soil, vegetation and atmosphere within the atmospheric boundary layer, as well as source and sink behaviors of ecosystems.

Actual evapotranspiration is assessed via surface energy balance at an hourly rate. However, a robust estimation of daily evapotranspiration from hourly values is required. Outcomes of surface energy balance are frequently determined via measures of eddy covariance latent heat flux. Surface energy balance can be applied on images acquired at different times and spatial resolutions. In addition, hourly actual evapotranspiration needs to be integrated at a daily rate for operational uses. Questions arise whether the validation of surface energy balance models can benefit from complementary in situ measures of latent heat flux and sap flow transpiration. Here, validation was driven by image acquisition time, spatial resolution, and temporal integration. Thermal and optical images were collected with a proximity-sensing platform on an olive orchard at different acquisition times. Actual latent heat fluxes from canopy and sap flux at tree trunks were measured with a flux tower and heat dissipation probes. The latent heat fluxes were then further analyzed. A surface energy balance was applied over proximity sensing images re-sampled at different spatial resolutions with resulting latent heat fluxes compared to in situ ones. A time lag was observed and quantified between actual latent heat fluxes from canopy and sap flux at the tree trunk. Results also indicate that a pixel resolution comparable to the average canopy size was suitable for estimating the actual evapotranspiration via a single source surface energy balance model. Images should not be acquired at the beginning or the end of the diurnal period. Findings imply that sap flow transpiration can be used to measure surface energy balance at a daily rate or when images are found at an hourly rate near noon, and the existing time lag between the latent heat flux at the canopy and the sap flow at the trunk does not need to be taken into account.

Modeling crop gross primary production (GPP) is critical to understanding the carbon dynamics of agro-ecosystems. Satellite-based studies have widely used production efficiency models (PEM) to estimate cropland GPP, wherein light use efficiency (LUE) is a key model parameter. One factor that has not been well considered in many PEMs is that canopy LUE could vary with illumination conditions. This study investigates how the partitioning of diffuse and direct solar radiation influences cropland GPP using both flux tower and satellite data. The field-measured hourly LUE under cloudy conditions was 1.50 and 1.70 times higher than that under near clear-sky conditions for irrigated corn and soybean, respectively. We applied a two-leaf model to simulate the canopy radiative transfer process, where modeled photosynthetically active radiation (PAR) absorbed by canopy agreed with tower measurements (R 2 = 0.959 and 0.914 for corn and soybean, respectively). Derived canopy LUE became similar after accounting for the impact of light saturation on leaf photosynthetic capacity under varied illumination conditions. The impacts of solar radiation partitioning on satellite-based modeling of crop GPP was examined using vegetation indices (VI) derived from MODIS data. Consistent with the field modeling results, the relationship between daily GPP and PAR × VI under varied illumination conditions showed different patterns in terms of regression slope and intercept. We proposed a function to correct the influences of direct and diffuse radiation partitioning and the explained variance of flux tower GPP increased in all experiments. Our results suggest that the non-linear response of leaf photosynthesis to light absorption contributes to higher canopy LUE on cloudy days than on clear days. We conclude that accounting for the impacts of solar radiation partitioning is necessary for modeling crop GPP on a daily or shorter basis. [ABSTRACT FROM AUTHOR]

Plant canopy spectral reflectance is measured at many observation towers to estimate the canopy’s biophysical and physiological features. When a wide-angle downward-facing spectrometer is used for tower-based measurements of reflected irradiance, it also captures reflections from the tower and near-horizon light from the sky within the field of view, leading to incorrect canopy reflectance estimates. However, the effects of this contamination have not been quantified. Therefore, we developed a retractable masking device to allow more accurate measurement of canopy reflectance by excluding the contamination. We continuously observed canopy reflectance of a Japanese larch ( Larix kaempferi ) forest from a flux tower by using the device alternately in masked and unmasked modes. In this paper, we describe the design of the masking device and demonstrate how tower contamination affected the measured canopy reflectance. We found that the canopy reflectance in the visible spectral region was overestimated by a maximum of more than 3 times the actual value due to the tower contamination, but that the near-infrared reflectance was affected much less. The contamination effects were substantial and complex, and the magnitude varied depending on the wavelength, solar zenith and azimuth angles, weather conditions, and canopy phenological status. Consequently, vegetation indices calculated from the spectral data were greatly affected. For example, the contaminated Normalized Difference Vegetation Index was 8–22% lower than that obtained with the masking device. We conclude that to accurately measure canopy reflectance via wide-angle spectroradiometers or photosynthetically active radiation sensors, a masking device similar to the one described in this paper is strongly recommended. [ABSTRACT FROM AUTHOR]

Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman-Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR). We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m at SMC and from 0.89 to 60.27 W m at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site. This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability. [ABSTRACT FROM AUTHOR]

Instantaneous radiative flux components across different climatic regions of USA were computed using Moderate Resolution Imaging Spectroradiometer (MODIS) land, atmosphere and geo-location products for clear sky pixels, colocated in time and space with flux towers. Satellite derived fluxes were validated using in-situ SURFRAD flux tower data of the respective sites. Significant positive correlation was observed between simulated and observed fluxes especially for incoming shortwave ( r = 0.94), incoming longwave ( r = 0.91) and outgoing shortwave ( r = 0.89) radiation. The root mean square errors (RMSE) were 50.7, 10.41, 18.54 and 33.50 Wm for incoming shortwave, outgoing shortwave, incoming longwave and outgoing longwave fluxes respectively with their corresponding relative RMSE of 0.11, 0.10, 0.06 and 0.08. The D-index and modeling efficiency (ME) varied from 0.87 to 0.96 and 0.61 to 0.85 respectively, indicated good performance of the present method. The coefficient of residual mass (CRM) for all the fluxes yielded values close to zero except incoming longwave radiation. Based on the statistical analysis and accumulative score highest rank was obtained for incoming longwave flux followed by outgoing shortwave, incoming shortwave and outgoing longwave fluxe. From the rank values it can be concluded that the present model predicted all the fluxes satisfactorily except the outgoing longwave flux [ABSTRACT FROM AUTHOR]